Publication | Closed Access
Arabesque
223
Citations
35
References
2015
Year
Unknown Venue
Cluster ComputingEngineeringDistributed AlgorithmsNetwork AnalysisGraph DatabaseDistributed Graph AnalyticsSemantic WebDistributed Data AnalyticsGraph ProcessingData ScienceData MiningSocial Network AnalysisGraph AlgorithmsKnowledge DiscoveryComputer ScienceGraph AlgorithmNetwork ScienceGraph TheoryFrequent SubgraphsBusinessGraph AnalysisBig Data
Distributed data processing platforms such as MapReduce and Pregel have substantially simplified the design and deployment of certain classes of distributed graph analytics algorithms. However, these platforms do not represent a good match for distributed graph mining problems, as for example finding frequent subgraphs in a graph. Given an input graph, these problems require exploring a very large number of subgraphs and finding patterns that match some "interestingness" criteria desired by the user. These algorithms are very important for areas such as social networks, semantic web, and bioinformatics.
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